A new pipeline approach is addressed for the task of detecting and tracking pixel-sized moving targets
from a time sequence of dynamic images of a high-noise environment. The trajectories of the moving
targets are unknown, but continuous and smooth. The image sequence contains significant, randomly
drifting background clutter, and is also contaminated by random sensor noise. The Pipeline Target Detection
Algorithm (PTDA) uses the temporal continuity of the smooth trajectories of moving targets, and
successfully detects and simultaneously tracks all the target trajectories by re-constructing them from the
time sequence of noisy images in a single targetframe in real time. The pipeline approach breaks the
constraint of straight line trajectory that most other algorithms require for the similar tasks and detects
and tracks any trajectories of arbitrary shapes as long as they are continuous and smooth. The pipeline
fashion of the aigorithm is a complete parallel distributedprocessing (PDP) type process, and therefore is
highly time efficient. It also agrees with the pattern that real image sequences are acquired, so is ideal for
real-time target detection and tracking.